Global Design Optimization of Microwave Circuits Using Response Feature Inverse Surrogates

Modern microwave design has become heavily reliant on full-wave electromagnetic (EM) simulation tools, which are necessary for accurate evaluation of microwave components. Consequently, it is also indispensable for their development, especially the adjustment of geometry parameters, oriented towards performance improvement. However, EM-driven optimization procedures incur considerable computational expenses, which may become impractical even in the case of local tuning, and prohibitive whenever global search is vital (e.g., multimodel tasks, simulation-based miniaturization, circuit re-design within extended ranges of operating frequencies). This work presents a novel approach to a computationally-efficient glob alized parameter tuning of microwave components. Our framework employs the response feature technology, along with the inverse surrogate models. The latter permit low-cost exploration of the parameter space, and identification of the most advantageous regions that contain designs featuring performance parameters sufficiently close to the assumed target. The initial parameter vectors rendered in such a way undergo then local, gradient-based tuning. The incorporation of response features allows for constructing the inverse model using small training data sets due to simple (weakly-nonlinear) relationships between the operating parameters and dimensions of the circuit under design. Global optimization of the two microstrip components (a coupler and a power divider) is carried out for the sake of verification. The results demonstrate global search capability, excellent success rate, as well as remarkable efficiency with the average optimization cost of about a hundred of EM simulations of the circuit necessary to conclude the search process.